Count transformation models

Siegfried, Sandra; Hothorn, Torsten

doi:10.1111/2041-210x.13383

Cited by 27 publications

(33 citation statements)

References 22 publications

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“…To estimate the determinants of intensity of CCAS, a Poisson model was employed. The Poisson model is the simplest and perhaps the most common method for modelling counts variables (Cameron and Trivedi, 1998;Siegfried and Hothorn, 2020). Poisson regression is used in this study because diagnostic tests revealed the absence of overdispersion and under dispersion.…”

Section: The Intensity Of Climate Change Adaptation Strategies Use Ammentioning

confidence: 99%

Understanding the determinants of climate change adaptation strategies among smallholder maize farmers in South-west, Nigeria

Adeagbo

Ojo

Adetoro

2021

Heliyon

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Climate is one of the most important factors in agricultural productivity, which could directly or indirectly influence productivity since the climate is linked to physiological processes. It is, therefore, essential to understanding the various strategies used by farmers to mitigate the adverse impact of climate change and the factors that influence maize farmers' adoption and intensity of climate change adaptation strategies among smallholder maize farmers in South-west Nigeria. In all, a sample of three hundred and thirty (311) smallholder maize farmers were interviewed. A double-hurdle count data model was employed to estimate the factors influencing farmers' adoption of adaptation strategies while accounting for selection bias with the plugging of inverse mill ratio (IMR) as a regressor. Significant variables such as household size, depreciation ratio, frequency of extension visits, access to extension, and non-farm income were factors influencing the adoption of climate change adaptation strategies among maize farmers. Age of the respondent, age square, household size, farm-based organization (FBO), non-farm income, climate information, access to credit, farmers residing in Osun State (location_Osun), distance to market significantly influenced the intensity of climate change adaptation strategies. This study, therefore, concluded that farm-level policy efforts that aim to improve rural development should focus on farmers’ membership in FBO, increase the visits of extension agents, encourage non-farm income and access to climate change information, particularly during the off-cropping season. Policies and investment strategies of the government should be geared towards supporting improved extension service, providing on-farm demonstration training, and disseminating information about climate change adaptation strategies, particularly for smallholder farmers in Nigeria.

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Section: The Intensity Of Climate Change Adaptation Strategies Use Ammentioning

confidence: 99%

Understanding the determinants of climate change adaptation strategies among smallholder maize farmers in South-west, Nigeria

Adeagbo

Ojo

Adetoro

2021

Heliyon

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“…To overcome 1st moment modeling, adjusting or ignoring the other moments of the distribution allows the most likely transformation (MLT) model [17]. Count data are particularly sensitive to the underlying assumption, e.g., for ties and variance heterogeneity structure, and therefore a special MLT approach for counts was recently proposed [28]. The CRAN package cotram offers flexible count transformation models where count responses may arise from various and complex data-generating processes.…”

Section: Most Likely Transformation Model Special For Count Datamentioning

confidence: 99%

Evaluation of histological findings with severity grade, to analyze toxicology in-vivo studies

Hothorn¹,

Weber²

2022

Preprint

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In-vivo toxicological studies are characterized by multiple primary endpoints with quite different scales. Whereas guidelines and publications provide various statistical tests for normally distributed endpoints (such as organ weights) and proportions (such as tumor rates), few approaches are available for graded histopathological findings, such as 0, +, ++, +++. This represents a basic contradiction of the statistical analysis because these graded findings sometimes show a high predictive value for potential toxic effects. Here we discuss different methods comparatively, especially from the viewpoints of i) designs for very small sample sizes and ii) interpretability by toxicologists. A new approach is recommended where a simultaneous test is performed over all class combinations of score levels, such as (0, +) vs (++, +++). Corresponding R code is provided by way of a data example.

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“…Therefore, a simple Freeman-Tukey transformation was used for the Dunnett test [11], [19] particularly appropriate for small n i . When the distribution is unknown and not only location effects should be considered, the most likely transformation model [12] can be used for the Dunnett test [14], the similar approach of continuous outcome logistic regression model [24] and the more specifically the cotram approach for count endpoints [30], i.e. most likely transformation methods for counts.…”

Section: Possible Biased Estimationsmentioning

confidence: 99%

Statistical analysis of no observed effect concentrations or levels in eco-toxicological assays with overdispersed count endpoints

Hothorn¹,

Kluxen²

2020

Preprint

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In (eco-)toxicological hazard characterization, the No Observed Adverse Effect Concentration or Level (NOAEC or NOAEL) approach is used and often required despite of its known limitations. For count data, statistical testing can be challenging, due to several confounding factors, such as zero inflation, low observation numbers, variance heterogeneity, over-or under-dispersion when applying the Poisson model or hierarchical experimental designs. As several tests are available for count data, we selected sixteen tests suitable for overdispersed counts and compared them in a simulation study. We assessed their performance considering data sets containing mixing distribution and over-dispersion with different observation numbers. It shows that there is no uniformly best approach because the assumed data conditions and assumptions are very different. However, the Dunnett-type procedure based on most likely transformation can be recommended, because of its size and power behavior, which is relatively better over most data conditions as compared to the available alternative test methods, and because it allows flexible modeling and effect sizes can be estimated by confidence intervals. Related R-code is provided for real data examples.

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Count transformation models

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 27 publications

References 22 publications

Understanding the determinants of climate change adaptation strategies among smallholder maize farmers in South-west, Nigeria

Understanding the determinants of climate change adaptation strategies among smallholder maize farmers in South-west, Nigeria

Evaluation of histological findings with severity grade, to analyze toxicology in-vivo studies

Statistical analysis of no observed effect concentrations or levels in eco-toxicological assays with overdispersed count endpoints

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